Here’s a full draft of the upcoming second edition of my “Data Visualization: A Practical Introduction”: socviz.co
Here’s a full draft of the upcoming second edition of my “Data Visualization: A Practical Introduction”: socviz.co
Ok I'm in a rabbit hole. If you search "how many decisions do we make in a day" the reported number is almost always 35,000, often reported that this is according to "multiple sources". Yet I can't actually find a single source that backs up that number. Anyone know where this number comes from?
The idea of the need for a control group may have been radical at the time, but trust me it's now generally accepted. This is not some fringe believe by methodological hardliners; it's established practice.
www.clinicaltrialsabundance.blog/p/clinical-t...
Worthwhile new essay "Mathematicians in the Age of AI" by Jeremy Avigad, CMU professor and director of the NSF Institute for Computer-Aided Reasoning in Mathematics (ICARM) at CMU: www.andrew.cmu.edu/user/avigad/...
Just realized that with Abolish ICE at +11, it's polling over 30 percentage-points ahead of Trump approval. Guess that means if the media wants to be representative, it should give at least like 40% more airtime to people who support abolishing ICE to Trump supporters
just realized that jupyter is probably dead as a concept. it's all md+scripts now.
Interested in giving a talk at @netsciconf.bsky.social but missed the main conference deadline? Submit your work to the Statistical Inference for Network Models (SINM) satellite! Send your one page abstract by March 15 and check out sinm.network for more details!
Looking forward to this!
Harsh Parikh will present work on how to transport estimated effects from one set of networks to another.
Shuangning Li will share new results on covariate adjustment in experiments.
So if you're working on networks+causality, consider participating. Abstracts due March 10th causnets.github.io
We've got 4 great invited speakers for our satellite event causnets.github.io at @netsciconf.bsky.social.
causnets.github.io/speakers/
In two nicely related talks, Christina Lee Yu & @vivianodavide.bsky.social will each present work on cluster-randomized designs in networks.
Is it primarily product differentiation? Did some econometrician in 1960 hear about Stein's theorem and go "we're having none of that; let's be the Amish"
Idgi, lexicographic preferences for unbiasedness over anything else is so alien.
If you're expecting there is something like a rigorous (or even just standard) diff-in-diff analysis in this paper, you may be disappointed...
Just a quick look suggests that:
1. Not a standard diff-in-diff plot, but the output some underreported regression "controlling" for GDP per capita.
2. Statistical inference is absent or neglects the country-level clustering.
I guess I'm more in the category of already having spent enough time reading other Twenge papers, so I have a very low prior on credibility
In some settings, you may have some population of effects that are perhaps subject to the same distribution of biases. Then you could do something like this paper. There it is still critical to have cases where you know (or have a good estimate of) the true effect in some cases.
My prior beliefs are that confounding is often large... So then it is helpful to have well identified studies in some particular area, which then might help us (if bias is small) be confident when doing observational research.
"If this event contributed anything, it simply made the ongoing death more obvious and less deniable for me personally. I consider the events of the last week a kind of death rattle of the old republic, the outward expression of a body that has thrown in the towel."
Don't skip the intro.
Part of the problem is that there are reasonably generic stories that are often true, and major problems. Like people try to do things that they think are good for them.
diagram of the Mechanical Turk showing how the operator could bend his body to avoid being seen as one cabinet door after the other was opened
Often seen in series of experimental studies, with improvements to validity brought out one at a time, but never all together. I liken this to the trick of Maelzel's Mechanical Turk: the human operator slid around inside the machine, never to be seen, as the showman opened one door after another.
Hegseth's "no stupid rules of engagement" (real quote) pairs well with Trump saying the US had some Khamenei successors in mind to back but the initial wave of American-Israeli strikes killed them.
Hmm I feel like people usually tell a story of why there is selection of a particular kind
time traveler from 12 months from now just sent me this
You'll understand our campaign more from this launch video than anything else. We gave it our heart and soul. Give it a watch.
The power of descriptive norms...
Short form videos of absurd conflicts — like this 45 second film from 1897 about a man fighting with a robot — are frying young people's brains
blogs.loc.gov/loc/2026/02/...
Amazing article. Anyone who understands what "R is dense" means should read this. And even the others, because the article explains it well! 👏 💯
Per protocol analysis strikes again!
Folks, if you randomize but then don‘t analyze some of the people who got randomized (maybe because they didn’t adhere to instructions, maybe because they dropped out), randomization will no longer do all the heavy causal inference lifting.
It was a lot of fun building this tool! Something that can help faculty with a small but tedious step in preparing an NSF grant proposal: the conflict of interests spreadsheet. Drag-and-drop simplicity ftw 🚤
A MN protester accused of assaulting ICE officer had case dropped after vids showed no assault.
LA protester accused of assaulting ICE w/ “hat” had case dropped; judge said govt acted in “bad faith”
In Chicago, 92 ppl were arrested for assault/impeding ICE; 0 convicted
DOJ keeps lying and losing: